Repetitive Transcranial Magnetic Stimulation (rTMS) Therapy in Parkinson Disease: A Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: Several studies have reported repetitive transcranial magnetic stimulation (rTMS) therapy as an effective treatment for the control of motor symptoms in Parkinson disease. The objective of the study is to quantify the overall efficacy of this treatment. TYPES: Systematic review and meta-analysis. LITERATURE SURVEY: We reviewed the literature on clinical rTMS trials in Parkinson disease since the technique was introduced in 1980. We used the following databases: MEDLINE, Web of Science, Cochrane, and CINAHL. PATIENTS AND SETTING: Patients with Parkinson disease who were participating in prospective clinical trials that included an active arm and a control arm and change in motor scores on Unified Parkinson's Disease Rating Scale as the primary outcome. We pooled data from 21 studies that met these criteria. We then analyzed separately the effects of low- and high-frequency rTMS on clinical motor improvements. SYNTHESIS: The overall pooled mean difference between treatment and control groups in the Unified Parkinson's Disease Rating Scale motor score was significant (4.0 points, 95% confidence interval, 1.5, 6.7; P = .005). rTMS therapy was effective when low-frequency stimulation (≤ 1 Hz) was used with a pooled mean difference of 3.3 points (95% confidence interval 1.6, 5.0; P = .005). There was a trend for significance when high-frequency stimulation (≥ 5 Hz) studies were evaluated with a pooled mean difference of 3.9 points (95% confidence interval, -0.7, 8.5; P = .08). rTMS therapy demonstrated benefits at short-term follow-up (immediately after a treatment protocol) with a pooled mean difference of 3.4 points (95% confidence interval, 0.3, 6.6; P = .03) as well as at long-term follow-up (average follow-up 6 weeks) with mean difference of 4.1 points (95% confidence interval, -0.15, 8.4; P = .05). There were insufficient data to statistically analyze the effects of rTMS when we specifically examined bradykinesia, gait, and levodopa-induced dyskinesia using quantitative methods. CONCLUSION: rTMS therapy in patients with Parkinson disease results in mild-to-moderate motor improvements and has the potential to be used as an adjunct therapy for the treatment of Parkinson disease. Future large, sample studies should be designed to isolate the specific clinical features of Parkinson disease that respond well to rTMS therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.024 | 0.051 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".